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    • Global surface-ocean partial pressure of carbon dioxide (pCO2) estimates from a machine learning ensemble: CSIR-ML6 v2019a (NCEI Accession 0206205)

    Metadata Updated: April 1, 2025

    This dataset contains surface-ocean partial pressure of carbon dioxide (pCO2) that the ensemble mean of six two-step clustering-regression machine learning methods. The ensemble is a combination of two clustering approaches and three regression methods. For the clustering approaches, we use K-means clustering (21 clusters) and open ocean CO2 biomes as defined by Fay and McKinley (2014). Three machine learning regression methods are applied to each of these two clustering methods. These machine learning methods are feed-forward neural-network (FFN), support vector regression (SVR) and gradient boosted machine using decision trees (GBM). The final estimate of surface ocean pCO2 is the average of the six machine learning estimates resulting in a monthly by 1° ⨉ 1° resolution product that extends from the start of 1982 to the end of 2016. Sea-air fluxes (FCO2) calculated from pCO2 are also presented in the data. The discrete boundaries of the clustering approach result in semi-discrete discontinuities in pCO2 and fCO2 estimates. These are smoothed by applying a 3 ⨉ 3 ⨉ 3 convolution (moving average) to the dataset in time, latitude and longitude.

    Access & Use Information

    License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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    Dates

    Metadata Date 2025-02-21T13:10:22Z
    Metadata Created Date March 19, 2021
    Metadata Updated Date April 1, 2025
    Reference Date(s) November 5, 2019 (publication)
    Frequency Of Update asNeeded

    Metadata Source

    Harvested from NOAA/NESDIS/ncei/accessions

    Other Data Resources

    Graphic Preview

    Preview graphic

    Additional Metadata

    Resource Type Dataset
    Metadata Date 2025-02-21T13:10:22Z
    Metadata Created Date March 19, 2021
    Metadata Updated Date April 1, 2025
    Reference Date(s) November 5, 2019 (publication)
    Responsible Party (Point of Contact)
    Contact Email
    Guid gov.noaa.nodc:0206205
    Access Constraints Cite as: Gregor, Luke; Lebehot, Alice D.; Kok, Schalk; Monteiro, Pedro M. S. (2019). Global surface-ocean partial pressure of carbon dioxide (pCO2) estimates from a machine learning ensemble: CSIR-ML6 v2019a (NCEI Accession 0206205). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/z682-mn47. Accessed [date]., Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
    Bbox East Long 180
    Bbox North Lat 89.5
    Bbox South Lat -89.5
    Bbox West Long -180
    Coupled Resource
    Frequency Of Update asNeeded
    Graphic Preview Description Preview graphic
    Graphic Preview File https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0206205
    Graphic Preview Type PNG
    Harvest Object Id 4e09f3b8-21b9-4635-a152-cf492a1c9c83
    Harvest Source Id c084a438-6f6b-470d-93e0-16aeddb9f513
    Harvest Source Title NOAA/NESDIS/ncei/accessions
    Licence accessLevel: Public
    Lineage
    Metadata Language eng
    Metadata Type geospatial
    Old Spatial {"type": "Polygon", "coordinates": [[[-180.0, -89.5], [180.0, -89.5], [180.0, 89.5], [-180.0, 89.5], [-180.0, -89.5]]]}
    Progress completed
    Spatial Data Service Type
    Spatial Reference System
    Spatial Harvester True
    Temporal Extent Begin 1982-01-01
    Temporal Extent End 2016-12-31

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